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Skill Authoring Workflow

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deanpeters
skill-authoring-workflow

Turn raw PM content into a compliant, publish-ready skill. Use when creating or updating a repo skill without breaking standards.

Overview

Publisherdeanpeters
RepositoryProduct-Manager-Skills
Skill nameskill-authoring-workflow
Stars
7.1K
Forks
851
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by deanpeters on GitHub. Read the source before you install it.

Installation

Install the Skill Authoring Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/skill-authoring-workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/deanpeters/Product-Manager-Skills.git /tmp/Product-Manager-Skills
mkdir -p .claude/skills
cp -r /tmp/Product-Manager-Skills/skills/skill-authoring-workflow .claude/skills/skill-authoring-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Authoring Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skill Authoring Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skill Authoring Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Purpose

Create or update PM skills without chaos. This workflow turns rough notes, workshop content, or half-baked prompt dumps into compliant skills/<skill-name>/SKILL.md assets that actually pass validation and belong in this repo.

Use it when you want to ship a new skill without "looks good to me" roulette.

Input

Bring the raw material and the intent — rough is fine; the workflow exists to get it the rest of the way:

  • Works best with: the source content (notes, transcript, framework, prompt sequence) or the existing skill you want to update
  • Also useful: the intended skill type (component/interactive/workflow), target audience, and any naming preference

If you supply this inline with your request (e.g., "turn research/pricing-workshop-notes.md into an interactive advisor"), the workflow starts at Phase 1 with that context — it won't re-ask for what you already gave. If you provide nothing, it opens by asking what content you want to turn into a skill and offers the entry modes from the facilitation protocol.

Example: Use skill-authoring-workflow: convert research/pricing-workshop-notes.md into an interactive pricing advisor.

Key Concepts

Dogfood First

Use repo-native tools and standards before inventing a custom process:

  • scripts/find-a-skill.sh
  • scripts/add-a-skill.sh
  • scripts/build-a-skill.sh
  • scripts/test-a-skill.sh
  • scripts/check-skill-metadata.py

Pick the Right Creation Path

  • Guided wizard (build-a-skill.sh): Best when you have an idea but not final prose.
  • Content-first generator (add-a-skill.sh): Best when you already have source content.
  • Manual edit + validate: Best for tightening an existing skill.

Definition of Done (No Exceptions)

A skill is done only when:

  1. Frontmatter is valid (name, description, intent, type)
  2. Section order is compliant (Purpose, Input, Key Concepts, Application, Examples, Common Pitfalls, References)
  3. Metadata limits are respected (name <= 64 chars, description <= 200 chars)
  4. Description says both what the skill does and when to use it
  5. The Input section says what the user can bring, shows an example invocation, tells the agent to use inline input instead of re-asking, and makes clear that arriving with partial or zero input is fine — in plain language, never runtime template syntax like $ARGUMENTS (rationale: CONTRIBUTING.md, "Why We Don't Use $ARGUMENTS")
  6. Intent carries the fuller repo-facing summary without replacing the trigger-oriented description
  7. Cross-references resolve
  8. README catalog counts and tables are updated (if adding/removing skills)

Facilitation Source of Truth

When running this workflow as a guided conversation, use workshop-facilitation as the interaction protocol.

It defines:

  • session heads-up + entry mode (Guided, Context dump, Best guess)
  • one-question turns with plain-language prompts
  • progress labels (for example, Context Qx/8 and Scoring Qx/5)
  • interruption handling and pause/resume behavior
  • numbered recommendations at decision points
  • quick-select numbered response options for regular questions (include Other (specify) when useful)

This file defines the workflow sequence and domain-specific outputs. If there is a conflict, follow this file's workflow logic.

Application

Phase 1: Preflight (Avoid Duplicate Work)

  1. Search for overlapping skills:
bash
./scripts/find-a-skill.sh --keyword "<topic>"
  1. Decide type:
  • Component: one artifact/template
  • Interactive: 3-5 adaptive questions + numbered options
  • Workflow: multi-phase orchestration

Phase 2: Generate Draft

If you have source material:

bash
./scripts/add-a-skill.sh research/your-framework.md

If you want guided prompts:

bash
./scripts/build-a-skill.sh

Phase 3: Tighten the Skill

Manually review for:

  • Clear "when to use" guidance
  • One concrete example — optimally two, from different business domains (one SaaS, one industrial/non-SaaS), so the framework visibly generalizes; reuse the repo's fictional universes (Fieldlight/Wrenchline for SaaS, Helix/Northfield/Corvid for industrial) and suffix the second file by domain (sample-industrial.md)
  • A template.md when the skill produces an artifact — the output schema as a copy/paste fill-in with quality checks
  • One explicit anti-pattern
  • No filler or vague consultant-speak

Phase 4: Validate Hard

Run strict checks before thinking about commit:

bash
./scripts/test-a-skill.sh --skill <skill-name> --smoke
python3 scripts/check-skill-metadata.py skills/<skill-name>/SKILL.md
python3 scripts/check-skill-triggers.py skills/<skill-name>/SKILL.md --show-cases

Phase 5: Integrate with Repo Docs

If this is a new skill:

  1. Add it to the correct README category table
  2. Update skill totals and category counts
  3. Verify link paths resolve

Phase 6: Optional Packaging

If targeting Claude custom skill upload:

bash
./scripts/zip-a-skill.sh --skill <skill-name>
# or zip one category:
./scripts/zip-a-skill.sh --type component --output dist/skill-zips
# or use a curated starter preset:
./scripts/zip-a-skill.sh --preset core-pm --output dist/skill-zips

Examples

Example: Turn Workshop Notes into a Skill

Input: research/pricing-workshop-notes.md
Goal: new interactive advisor

bash
./scripts/add-a-skill.sh research/pricing-workshop-notes.md
./scripts/test-a-skill.sh --skill <new-skill-name> --smoke
python3 scripts/check-skill-metadata.py skills/<new-skill-name>/SKILL.md

Expected result:

  • New skill folder exists
  • Skill passes structural and metadata checks
  • README catalog entry added/updated

Anti-Pattern Example

"We wrote a cool skill, skipped validation, forgot README counts, and shipped anyway."

Result:

  • Broken references
  • Inconsistent catalog numbers
  • Confusion for contributors and users

Common Pitfalls

  • Shipping vibes, not standards.
  • Choosing workflow when the task is really a component template.
  • Bloated descriptions that exceed upload limits.
  • Descriptions that say what the skill is but not when Claude should trigger it.
  • Descriptions that silently hit the 200-char limit and get cut off mid-thought.
  • Letting intent become a substitute for a weak trigger description.
  • Forgetting to update README counts after adding a skill.
  • Treating generated output as final without review.

References

  • README.md
  • AGENTS.md
  • CLAUDE.md
  • docs/Building PM Skills.md
  • docs/Add-a-Skill Utility Guide.md
  • Anthropic's Complete Guide to Building Skills for Claude
  • scripts/add-a-skill.sh
  • scripts/build-a-skill.sh
  • scripts/find-a-skill.sh
  • scripts/test-a-skill.sh
  • scripts/check-skill-metadata.py
  • scripts/check-skill-triggers.py
  • scripts/zip-a-skill.sh

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skill Authoring Workflow AI skill do?

Turn raw PM content into a compliant, publish-ready skill. Use when creating or updating a repo skill without breaking standards.

Why use Skill Authoring Workflow on TypingMind?

Because you install it once and use it with any model. Skill Authoring Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skill Authoring Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/skill-authoring-workflow. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skill Authoring Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skill Authoring Workflow?

As many as you like. As long as a model supports skills, you can use Skill Authoring Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skill Authoring Workflow AI skill free?

It is published on GitHub by deanpeters. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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